From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...

Defections, secret conversations, deal talks that fizzled and a battle for control: The turmoil at Thinking Machines Lab is the artificial intelligence industry’s latest drama.

Adversarial examples—images subtly altered to mislead AI systems—are used to test the reliability of deep neural networks.

This workshop will consider several applications based on machine learning classification and the training of artificial neural networks and deep learning.

Abstract: Malware poses a significant threat to network and information system security, particularly in industrial Internet of Things (IIoT) environments, where embedded systems and edge devices ...

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...

This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...

The Recentive decision exemplifies the Federal Circuit’s skepticism toward claims that dress up longstanding business problems in machine-learning garb, while the USPTO’s examples confirm that ...

The ESP32-Stick-PoE-A-Cam(N16R8) is an open-source ESP32-S3 development board with Ethernet, camera, and active PoE support designed for machine learning applications. Compared to similar boards like ...

Logistic Regression is a widely used model in Machine Learning. It is used in binary classification, where output variable can only take binary values. Some real world examples where Logistic ...